Trabajo Practico 1

Paso 1: Cargamos los datos de entrada

ar_properties <- read.csv(unz('ar_properties.zip', 'ar_properties.csv'), row.names=NULL, stringsAsFactors=TRUE)
head(ar_properties)

Error in gregexpr(calltext, singleline, fixed = TRUE) : 
  regular expression is invalid UTF-8

nrow(ar_properties)

[1] 388891

Paso 2: Filtramos los datos

library(tidyverse)

Registered S3 method overwritten by 'dplyr':
  method           from
  print.rowwise_df     
-- Attaching packages --------------------------------------- tidyverse 1.2.1 --
v ggplot2 3.2.1     v purrr   0.3.2
v tibble  2.1.3     v dplyr   0.8.3
v tidyr   1.0.0     v stringr 1.4.0
v readr   1.3.1     v forcats 0.4.0
-- Conflicts ------------------------------------------ tidyverse_conflicts() --
x dplyr::filter() masks stats::filter()
x dplyr::lag()    masks stats::lag()

ar_properties <- ar_properties %>% filter(
                    l1             ==   'Argentina' &
                    l2             ==   'Capital Federal' &
                    currency       ==   'USD' &
                    operation_type ==   'Venta' &
                    property_type  %in% c('Casa', 'Departamento', 'PH'))

ar_properties <- ar_properties %>% select(
                    id,
                    l3,
                    rooms,
                    bedrooms,
                    bathrooms,
                    surface_total,
                    surface_covered,
                    price,
                    property_type )

nrow(ar_properties)

[1] 61905

for (columnName in colnames(ar_properties))
{
  print(paste0(str_pad(columnName, 20, side='right'), '=>', length(unique(ar_properties[[columnName]]))))
}

[1] "id                  =>61905"
[1] "l3                  =>58"
[1] "rooms               =>24"
[1] "bedrooms            =>25"
[1] "bathrooms           =>15"
[1] "surface_total       =>671"
[1] "surface_covered     =>573"
[1] "price               =>4095"
[1] "property_type       =>3"

#sort(unique(as.character(ar_properties$l3)))

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